Designing a Data Warehouse for Cyber Crimes

One of the greatest challenges facing modern society is the rising tide of cyber crimes. These crimes, since they rarely fit the model of conventional crimes, are difficult to investigate, hard to analyze, and difficult to prosecute. Collecting data in a unified framework is a mandatory step that will assist the investigator in sorting through the mountains of data. In this paper, we explore designing a dimensional model for a data warehouse that can be used in analyzing cyber crime data. We also present some interesting queries and the types of cyber crime analyses that can be performed based on the data warehouse. We discuss several ways of utilizing the data warehouse using OLAP and data mining technologies. We finally discuss legal issues and data population issues for the data warehouse

Designing a Data Warehouse for Cyber Crimes

One of the greatest challenges facing modern society is the rising tide of cyber crimes. These crimes, since they rarely fit the model of conventional crimes, are difficult to investigate, hard to analyze, and difficult to prosecute. Collecting data in a unified framework is a mandatory step that will assist the investigator in sorting through the mountains of data. In this paper, we explore designing a dimensional model for a data warehouse that can be used in analyzing cyber crime data. We also present some interesting queries and the types of cyber crime analyses that can be performed based on the data warehouse. We discuss several ways of utilizing the data warehouse using OLAP and data mining technologies. We finally discuss legal issues and data population issues for the data warehouse

Data Mining in Promoting Flight Safety

The incredible rapid development to huge volumes of air travel, mainly because of jet airliners that appeared to the sky in the 1950s, created the need for systematic research for aviation safety and collecting data about air traffic. The structured data can be analysed easily using queries from databases and running theseresults through graphic tools. However, in analysing narratives that often give more accurate information about the case, mining tools are needed. The analysis of textual data with computers has not been possible until data mining tools have been developed. Their use, at least among aviation, is still at a moderate level. The research aims at discovering lethal trends in the flight safety reports. The narratives of 1,200 flight safety reports from years 1994 – 1996 in Finnish were processed with three text mining tools. One of them was totally language independent, the other had a specific configuration for Finnish and the third originally created for English, but encouraging results had been achieved with Spanish and that is why a Finnish test was undertaken, too. The global rate of accidents is stabilising and the situation can now be regarded as satisfactory, but because of the growth in air traffic, the absolute number of fatal accidents per year might increase, if the flight safety will not be improved. The collection of data and reporting systems have reached their top level. The focal point in increasing the flight safety is analysis. The air traffic has generally been forecasted to grow 5 – 6 per cent annually over the next two decades. During this period, the global air travel will probably double also with relatively conservative expectations of economic growth. This development makes the airline management confront growing pressure due to increasing competition, signify cant rise in fuel prices and the need to reduce the incident rate due to expected growth in air traffic volumes. All this emphasises the urgent need for new tools and methods. All systems provided encouraging results, as well as proved challenges still to be won. Flight safety can be improved through the development and utilisation of sophisticated analysis tools and methods, like data mining, using its results supporting the decision process of the executives.Lentoliikenne kasvoi huomattavasti 1950-luvulla pääasiassa suihkumatkustajakoneiden myötä, mikä aiheutti poikkeamatietojen järjestelmällisen keräämisen ja tutkimuksen tarpeen. Määrämuotoinen tieto voidaan helposti analysoida tietokantakyselyillä esittäen tulokset käyttäen graafisia työkaluja, mutta tekstianalyysiin, jonka avulla tapauksista saadaan usein tarkempia tietoja, tarvitaan louhintatyökaluja. Tekstimuotoisen tiedon automaattinen analysointi ei ole ollut mahdollista ennen louhintatyökalujen kehittämistä. Silti niiden käyttö, ainakin ilmailun piirissä, on edelleen vähäistä. Tutkimuksen tarkoituksena oli havaita vaarallisia kehityskulkuja lentoturvallisuusraporteissa. 1 200 lentoturvallisuusraportin selostusosiot vuosilta 1994 –1996 käsiteltiin kolmella tekstinlouhintatyökalulla. Yksi näistä oli täysin kieliriippumaton, toisessa oli lisäosa, jossa oli mahdollisuus käsitellä suomen kieltä ja kolmas oli rakennettu alun perin ainoastaan englanninkielisen tekstin louhintaan, mutta espanjan kielellä saavutettujen rohkaisevien tulosten pohjalta päätettiin kokeilla myös suomenkielistä tekstiä. Lento-onnettomuuksien määrä liikenteeseen nähden on vakiintumassa maailmanlaajuisesti katsottuna ja turvallisuustaso voidaan katsoa tyydyttäväksi. Kuitenkin liikenteen kasvaessa myös onnettomuuksien määrä lisääntyy vuosittain, mikäli lentoturvallisuutta ei kyetä parantamaan. Turvallisuustiedon kerääminen ja raportointijärjestelmät ovat jo saavuttaneet huippunsa. Analysoinnin parantaminen on avain lentoturvallisuuden parantamiseen. Lentoliikenteen on ennustettu kasvavan 5 – 6 prosenttia vuodessa seuraavien kahden vuosikymmenen ajan. Samana aikana lentoliikenne saattaa kaksinkertaistua jopa vaatimattomimpien talouskasvuennusteiden mukaan. Tällainen kehitys asettaa lentoliikenteen päättäjille yhä kasvavia paineita kiristyvän kilpailun, polttoaineiden hinnannousun ja liikenteen kasvun aiheuttaman onnettomuuksien määrän vähentämiseksi. Tämä korostaa uusien menetelmien ja työkalujen kiireellistä tarvetta. Kaikilla louhintajärjestelmillä saatiin rohkaisevia tuloksia mutta ne nostivat samalla esille haasteita, jotka tulisi vielä voittaa. Lentoturvallisuutta voidaan vielä parantaa käyttämällä tässä esille tuotuja analyysimenetelmiä ja –työkaluja kuten tiedonlouhintaa ja soveltamalla näin saatuja tuloksia johdon päätöksenteon tukena.Siirretty Doriast

SHORT TERM CRIME FORECASTING FOR PREVENTION OF CRIMES: A STUDY OF SATARA DISTRICT

Abstract: This research paper highlights the importance of crime forecasting in crime prevention in Satara District police station jurisdiction. Crime investigation and prevention has very significant role of police system in any country. The crime data has been stored and processed using Common Integrated Police Application [CIPA] and it become useful for getting the criminal information but it does not help for the purpose of designing an action to prevent the crime, it has become a major challenge for police system to detect and prevent crimes and criminals. There is no any kind of information is available before happening of such criminal acts and it result into increasing crime rate. The presented paper highlights the use of Forecasting technique to identify the crime in a specific area which can be useful for crime prevention

Cyber-crime Science = Crime Science + Information Security

Cyber-crime Science is an emerging area of study aiming to prevent cyber-crime by combining security protection techniques from Information Security with empirical research methods used in Crime Science. Information security research has developed techniques for protecting the confidentiality, integrity, and availability of information assets but is less strong on the empirical study of the effectiveness of these techniques. Crime Science studies the effect of crime prevention techniques empirically in the real world, and proposes improvements to these techniques based on this. Combining both approaches, Cyber-crime Science transfers and further develops Information Security techniques to prevent cyber-crime, and empirically studies the effectiveness of these techniques in the real world. In this paper we review the main contributions of Crime Science as of today, illustrate its application to a typical Information Security problem, namely phishing, explore the interdisciplinary structure of Cyber-crime Science, and present an agenda for research in Cyber-crime Science in the form of a set of suggested research questions

Beyond Awareness: Using Business Intelligence to Create a Culture of Information Security

Employees, intentionally or not, cause a large percentage of security incidents. For an organization to be secure there must be a culture of information security, meaning that employees make good security-related decisions. Business intelligence (BI) systems, with their ability to promote change through goal-setting and accountability, could help create a culture of information security, if implemented appropriately. This paper provides an overview of information security culture and business intelligence, and explains what will be needed if BI is to be used to help organizations develop a security-aware culture

A COMPREHENSIVE GEOSPATIAL KNOWLEDGE DISCOVERY FRAMEWORK FOR SPATIAL ASSOCIATION RULE MINING

Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space–time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process. Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining. To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining